Quantifying the independent contributions of climate and land use change to ecosystem services

doi:10.21203/rs.3.rs-2357146/v1

Ecosystem Services (ESs) are the embodiment of human welfare and play an important part in supporting the sustainable development of human society and regions. Climate change (CLC) and land use change (LUCC) are the most important factors influencing ESs. However, few studies have been devoted to differentiate their independent contributions on ESs. Based on the meteorological, soil, land use, and remote sensing data of Liaoning Province from 2000 to 2020, the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST) model and Carnegie-Ames Stanford Approach(CASA) model was applied to construct a scenario simulation framework with three hypotheses(only CLC effect, only LUCC effect and the combined effect of CLC and LULC) to differentiate the independent contributions betwwen CLC and LUCC to net primary productivity(NPP), water yield (WY) and soil retention (SR) change and their dynamic change. The results showed that under the joint effect of CLC and LULC, NPP, WY and SR in Liaoning Province showed a rising trend from 2000 to 2020, increasing by 124.62gC/m², 30.64mm/a and 0.63t/km², respectively. In only CLC effect scenario, WY and SR changed by 6.24% and 2%, respectively, which was more significant than in only LUCC effect scenario. By contrast, NPP changed by 25.71% in only LUCC effect scenario, which was more significant than in only CLC effect scenario. Overall, CLC was the dominant factor of WY and SR change in Liaoning Province, with a independent contributions rate of 81.79%-84.02% and 73.57%-85.44%, respectively, whereas LUCC was the dominant factor of NPP change, with a independent contributions rate of 86.12%-92.50%. The decreased precipitation and the increased temperature were two primary reasons of the fluctuation in the independent contributions rate of CLC to WY and SR change, while the large area damage of forest land and rapid urbanization were two primary reasons of the fluctuation in the independent contributions rate of LUCC to NPP change. The study revealed the affect of different climatic conditions to ESs and the strong conflict between urbanization and ecosystem service provision and provided a theoretical foundation for the increase of ESs and regional sustainable development in Liaoning Province.

ecosystem services

scenario simulation

independent contribution

climate change

land use change

Ecosystem services (ESs) refer to the benefits that humans derive from their surrounding ecosystem, usually conosist of provisioning services (e.g. raw material supply and food supply), regulating services (e.g. flood storage and regulation of atmosphere), supporting services (e.g. habitat support and groundwater recharge), and cultural services (e.g. research, education, tourism) (Boyd & Banzhaf, 2007; Costanza et al., 2017;Wang et al., 2022). ESs can support the completeness of ecosystems and provide a fundamental life support to people, so it is considered a connection bridge between human society and ecosystems(Deng et al., 2016; Tolessa et al., 2017). However, with the global environmental change and the rapid development of social economy in recent years, about 60% ESs is undergoing decreasing in our earth(Balkanlou et al., 2020; Costanza et al., 2014; Jiang et al., 2019; Tian et al., 2022). The degradation of ESs disrupts the supply-demand link between human and ecosystem, thus adversely influencing human welfare and the region sustainable development (Zhang et al., 2021). Therefore, analyzing the change in ESs and the impact mechanism behind them is helpful for policymakers to provide effective ecosystem service management strategies, which is important for increasing human welfare and the region sustainable development.

CLC is one of the most critical factors affecting ESs(Guo et al., 2022; Ma et al., 2021; Weiskopf et al., 2020). It influences ESs by changing hydrological-energy distribution and carbon emission(Hao & Yu, 2018; Sönmez et al., 2016). For the past few years, lots of regions have been moving heavily toward drying and warming trends due to the increased global climate change, while dramatically increased the risk of desertification, food scarcity and water scarcity(Y. Wang et al., 2021; Y. Wang & Dai, 2020). Previous researches have examined the influences of CLC on ESs, which dominated by reducing of precipitation and increasing of temperature. For example, decreasing of precipitation has led to a decreased in water yield by 22.08% in semi-arid regions of China from 1995 to 2015(Wu et al., 2021). Increasing of temperature has decreased grassland ESs by 33% in Inner Mongolia Autonomous Region of China from 1989 to 2011(H. Wang et al., 2016). Trade-offs and synergistic relationships among ESs had significant spatial heterogeneity in response to future climate change in European forest ecosystem(Mina et al., 2017). LUCC is another important factor to affect ESs through altering the physical properties of soil, for examble albedo, surface soil properties and evapotranspiration to change ecological processes and thereby affecting ESs(Claussen et al., 2001; Lei et al., 2021; J. Li et al., 2018). In recent years, with the intensification of human activities represented by urbanization, significant change in land use patterns have occurred around the world(X. Wang et al., 2022). Previous researches have proven the change in land use intensity(Inkoom et al., 2018), patterns(F. Wang et al., 2021), and types(Polasky et al., 2011) can significantly affect ESs.For instance, although the continuous extension of building land increased the total ESs in Beijing, its regulating and supporting services showed a decreasing trend(Peng et al., 2017). When dry land was transferred to forest land, soil conservation was enhanced in Guangxi Province(Qiu et al., 2021). With the increasing land use intensity, the water production, carbon storage and habitat quality in Shanxi Province showed a significant decreasing trends(Hu et al., 2021). Thereby, it’s very important to explore the effect of CLC and LUCC to ESs for regional sustainable development and the improvement of ESs.

Liaoning Province is situated in the southern region of Northeast China, where is a crucial component of China's Bohai Economic Circle(Chen et al., 2017). In these years, under the guidance of the Chinese government policy to revitalize the old industrial areas of Northeast China and the Liaoning Coastal Economic Development Zone Program, Liaoning Province has experienced rapid development of various industries, high economic growth and accelerated urbanization, and thus making it become the biggest provincial level economic entity in Chinese northeastern regions(Chen et al., 2017). Rapid economic progress and the increased urbanization has resulted the destruction of many natural ecosystems, causing in significant degradation of ESs in Liaoning province(Li et al., 2022). At the same time, climate fluctuations in Liaoning province in recent years have significantly impacted ecosystem services such as vegetation productivity and water security. However, the independent contributions of CLC and LUCC on ESs change in Liaoning Province and their dynamic change are still unclear, and there are no relevant studies to analyze them. Therefore, it is necessary to further research on this. The shadow engineering method(Guo et al., 2022), market value method(Pirard & Lapeyre, 2014) and emergy method(Song et al., 2021)has been widely used to calculate ESs in different ecosystem, but biophysical models are inherently more sensitive to ecosystem processes and climate change, and they are better at simulating ESs change under different climate and land use conditions. Such as, Wang et al. applied the Invest and CASA model to simulate ESs of Loess Plateau region based on different meteorological and land use data from 2000–2018(Wang et al., 2022). Li et al. studied the trade-offs within multiple ESs of Zhangye city through the Invest model and made recommendations for the healthy urban development (Li et al., 2020). In this study, a new scenario simulation framework from a dynamic change perspective was constructed by combining model InVEST and CASA model to differentiate the independent contributions of CLC and LUCC to the NPP, WY and SR change in Liaoning Province from 2000 to 2020. The framework was divided into two independent parts from 2000 to 2010 and from 2010 to 2020. Three simulation scenarios were designed, included only CLC effect, only LUCC effect and the combined effect of CLC and LULC.

The purposes of the study were: (1) to analyze the spatio-temporal change of climate and land use in Liaoning Province during 2000 and 2020; and (2) to analyze the spatio-temporal change in ESs under the joint influencet of climate and land use change; and (3) to compare the ESs change under different simulation scenarios; and (4) to quantify the independent contributions of CLC and LUCC on ESs change and their dynamics; and (4) to make a discussion on the impact mechanism of ESs change and make effective management recommendations for improving ESs and regional sustainable development based on our findings.

2.1. Study area

Liaoning Province is located in the south of Northeast China which convers an area of 148,000km². As the northern extreme maritime province in China, Liaoning is the biggest provincial economy in Northeast China(Li et al., 2022), and it’s referred to as the "Golden Triangle" due to its unique terrain and strategic position as well(Li et al., 2022).The topography of Liaoning is dominated by mountains and hills, covering more than 59.5% of the province, with elevations decreasing from east to west to the center. Liaoning Province belongs to a monsoon climate of mediium latitudes and it has an average annual precipitation and temperature of 327-1270mm and 5.6–10.9°C. In 2020, Liaoning Province had a resident population of 42,591,400 and a regional gross domestic product of 2,511.5 billion RMB.

2.2. Data

This research mainly used: land use and cover data, DEMdata, NDVI data, soil data, vegetation cover data, and meteorological data. These data sources used were listed in Table 1. All MODIS data was batch processed by MODIS Reprojection Tool software. All data has been uniform resolution to 30m×30m through resampling.

2.3. ESs evaluation

2.3.1. NPP

NPP is the quality of dry organic matter cumulative unit area of vegetation per over a period of time. It has a important part in the cycle of nutrients and the supporting services(Luo et al., 2018;Zhang et al., 2022). In ENVI5.3 platform, a NPP calculation plug-in under the CASA-based model designed by Wen-Quan et al has been applied to calculate the NPP(Wen-Quan et al., 2007):

NPP(x, t) = APAR(x, t) × ε(x, t) (1)

where NPP (x, t) is the amount of NPP of picture element x at moment t (gC/m²), APAR (x, t) is the effective radiation of photosynthesis assimilated by picture element x at moment t (MJ/m²), ε (x, t) is the real optical energy utility ratio of picture element x at moment t (gC/MJ). The NDVI, vegetation type and meteorological data required for the model was obtained from general data in the Liaoning Province and the static parameter files were obtained by consulting to previous researches(Wen-Quan et al., 2007).

2.3.2. WY

WY represents an capacity that different ecosystem to storage water generated from rainfall while ignoring the effects of surface runoff. It plays an important part in agricultural production and improves people living quality. It’s the basis for management of water resources as well(Cudennec et al., 2007;Yang et al., 2019). WY was evaluated in "Annual Water Yield" part in the Invest 3.10.2 platform by the Budyko water equilibrium formula (Deng et al., 2021):

Y _x = (1-AET _x /P _x ) × P _x (2)

Where Y_x is the WY of image element x (mm/a), AET_x is the real evapotranspiration of image element x (mm/a), and P_x is the rainfall of image element x (mm/a).

2.3.3. SR

SR is an ability of different ecosystem to command soil denudation to guard soil erosion and it is often regraded as the deviation value within potential and real denudation (Xiao et al., 2017). SR was computed in "sediment delivery ratio" part in the Invest 3.10.2 platform based on the amendatory equation of soil loss(Wang et al., 2021):

SR = R×K×LS×(1-C×P) (3)

where SR refers to the soil retention (t/km²), R is the precipitation erosivity factor (MJ.mm/ km².h), K is soil erodibility factor (t. km².h/ km².MJ.mm), LS is the slope and slope length factor, C is the vegetation cover and management factor, and P is the soil retention measure factor. The R and K factor required for the model was calculated from meteorological and soil data of this research area and the C and P factor was obtained by referring to previous studies (Y. Wang & Dai, 2020).

2.4. Scenario simulation framework

The independent contributions between CLC and LUCC to ESs could be calculated by simulating diverse scenarios. In this study, based on the climate and land use data in 2000–2020, eight simulation scenarios were established, which were divided into two parts: 2000–2010 and 2010–2020(Table 2). In 2000–2010, scenario 1: the input data was both climate and land use in 2000, which was the reference scenario(Re₁); scenario 2: the input data was climate in 2000 and land use in 2010, which was the only LUCC effect scenario(ES₁); scenario 3: the input data was land use in 2000 and climate in 2010, which was the only CLC effect scenario(ES₂); scenario 4: the input data was both climate and land use in 2010, which was the combined effect of CLC and LULC scenario(ES₃). In 2010–2020, scenario 5: the input data was climate and land use in 2010, which was the reference scenario(Re₂); scenario 6: the input data was climate in 2010 and land use in 2020, which was the only LUCC effect scenario(ES₄); scenario 7: the input data was land use in 2010 and climate in 2020, which was the only CLC effect scenario(ES₅); scenario 8: the input data was both climate and land use in 2020, which was the combined effect of CLC and LULC scenario(ES₆).

Through scenario simulation, the independent contributions of CLC and LUCC on ESs could be calculated by the following equation:

∆ES = ES _m -Re _n (4)

C _clc =(ES _a -Re _n )/∆ES×100% (5)

C _LUCC =(ES _b -Re _n )/∆ES×100% (6)

where, ES_m is the ESs in the combined effect CLC and LUCC scenario, Re_n is the ESs in the reference scenario, ES_a is the ESs in the only CLC effect scenario, ES_b is the ESs in the only LUCC effect scenario, ∆ES is the ESs change under the combined effect of CLC and LUCC, C_CLC is the independent contributions of CLC on ESs change, and C_LUCC is the independent contributions of LUCC on ESs change.

3.1. Climate change

During 2000 and 2020, the average precipitation and temperature in Liaoning Province showed an overall rising trend (Fig. 3). From 2000 to 2010, the average precipitation increased by 13.06%, with an increased rate of 6.97 mm/a. From 2010 to 2020, it decreased by 4.60%, with a decrease rate of 2.78 mm/a. From 2000 to 2010, the precipitation-intensive area shifted from the southwest to the east. From 2010 to 2020, it extended from the east to the north. From 2000-2020, the western part in Liaoning Province was a precipitation-deficient area. The maximum and minimum temperature in Liaoning province increased by 0.02°C and 0.31°C, respectively, from 2000 to 2010, whereas it increased by 0.97°C and 0.78°C, respectively, from 2010 to 2020. All province appeared a spacial distribution of low temperature in the north and high temperature in the south.

3.2. Land use change

During the two phases of 2000-2010 and 2010-2020, cropland was the primary land use type in Liaoning Province, with an average percentage of 47.50%, mainly in the central and northern parts, followed by forest land at 29.23%, mainly in the eastern and western (Table 3, Fig. 4). The area of unused land grew the fastest, with an increasing rate of 56.60%. In contrast, the area of cropland decreased the fastest, with a decreasing rate of 3.89% (Table 3). The area of both grassland and water body showed a trend of improving and then declining. The land use transfer matrix showed that within the two phases, cropland was the largest type of land type transferred out, accounting for 45.87% of the overall transferred area, followed by forest land at 24.09% (Fig. 5). The main transfer directions of cropland were 42.31% for forest land and 31.81% for construction land, and the main transfer directions of forest land were 54.44% for cropland and 19.20% for construction land (Fig. 5). The overall transfer area in 2010-2020 increased by 7.13% compared to 2000-2010.

3.3. Change in ESs

3.3.1. NPP change

During 2000 and 2020, NPP showed a rising trend in Liaoning Province. In 2000, 2010 and 2020, the average NPP were 484.76 gC/m², 539.15 gC/m² and 609.38 gC/m², respectively (Fig. 6). During the two phases of 2000-2010 and 2010-2020, the average NPP increased by 11.22% and 13.03%, respectively, with an increasing rate of 5.44gC/m²/a and 7.02gC/m²/a, and the maximum value also increased by 13.22% and 11.89%, respectively. From 2000 to 2010, the increased areas of NPP accounted for 78.03% in the total study area, major distributed in the middle, southern and southwestern regions, whereas the increase areas of NPP value about 51.37% of the all study area from 2010 to 2020, major distributed in the northern regions and westeren regions.

3.3.2. WY change

WY also appeared an uptrend in Liaoning Province during 2000 and 2020. The mean WY increased from 486.45mm to 547.66mm in 2000-2010, with an increasing rate of 6.12mm/a. From 2010-2020, the mean WY decreased from 547.66mm to 516.79mm during 2010 to 2020, with a decreasing rate of 3.09mm/a. WY showed a similar spatial distribution to the precipitation. From 2000 to 2010, the increase region of WY value about 51.22% of the all study region, major distributed in the northern, southern and eastern, whereas the increase area of WY value accounted for 21.35% of the all study area from 2010 to 2020, major distributed in the northern and southern.

3.3.3. Soil retention

Overall, SR also showed an upward trend during 2000 to 2020. From 2000 to 2010, the average SR increased from 26.41t/km² to 28.08 t/km², mainly concentrated in the east, whereas the average SR decreased from 28.08t/km² to 26.87 t/km² from 2010 to 2020, mainly concentrated in the southeast. The SR high value areas were mainly distributed in the east and west side of Liaoning Province, and the distribution was semblable in each period. From 2000 to 2010, the rising areas of SR accounted for 14.85% of the all study area, and the rising areas of SR accounted for 10.36% from 2010 to 2020.

3.4. ESs change in different simulation scenarios

The results of simulation scenarios in 2000-2010 showed that in only CLC effect scenario NPP, WY and SR changed by 4.08gC/m², 51.43mm/a and 1.23t/km² compared to Re₁, respectively, while in only 2010 LUCC effect scenario, NPP, WY and SR changed by 50.31gC/m², -16.91mm/a and 0.44t/km², respectively. In contrast, the results of simulation scenarios in 2010-2020 showed that in only CLC effect scenario, NPP, WY and SR changed by 9.75gC/m², -25.58mm/a and -1.03t/km² compared to Re₂, respectively, while in only in only 2020 LUCC effect scenario, NPP, WY, and SR changed by 60.48gC/m², -5.59mm/a and -0.18t/km², respectively.

3.5. The independent contributions of CLC and LUCC to ESs change and their dynamics

We further differentiated the independent contributions of CLC and LUCC to ESs change and their dynamics change. From 2000 to 2010, the independent contributions of CLC to WY and SR were 84.02% and 73.57%, respectively, which were higher than those in LUCC, whereas, the independent contributions of LUCC to NPP change were 92.50%. However, the independent contributions of CLC to WY change decreased by 2.65%, and to SR change increased by 16.13%, while the independent contributions of LUCC to NPP change decreased by 6.90% in 2010-2020. During 2000 to 2020, the average contributions of CLC to WY and SR change were 82.91% and 79.51%, respectively, and the average contributions of LUCC to NPP were 89.31%. Overall, CLC was the dominant factor for WY and SR change, whereas LUCC was the dominant factor for NPP change.

4.1. Impacts of CLC on ESs

The results of this study showed that CLC was the dominant factor to WY and SR change in Liaoning Province from 2000 to 2020, which was generally consistent with the consequences of previous studies(Dai et al., 2020; Ferreira et al., 2019; Y. Wang & Dai, 2020). The results of the scenario simulation in this study further indicated that with the decreasing of precipitation in Liaoning Province during 2010–2020 (-2.78 mm/a), the independent contributions of CLC to WY change also decreased by 2.65%. The results reflected that under the stress of precipitation shortage, CLC had a significant inhibitory effect on WY. A study also showed that the decreasing of precipitation could directly reduce the water input within the region, thus decreasing the runoff volume and ultimately causing some adverse effects on WY(Bai et al., 2019). From 2000 to 2020, the independent contributions of CLC to SR change also increased by 16.13% with the increasing of temperature, indicating that the increasing of temperature could make CLC have more positive effects for SR. According to previous studies, densely vegetated areas could reduce the risk of soil erosion more effectively, and the increasing of temperature would better promote vegetation growth, which improved the regional SR(Hasan & Kumar, 2021). However, densely vegetated areas tended to have higher evapotranspiration coefficients, and the water trapped in the forest canopy was more likely to evaporate. It indicated that the increasing of temperature would offset part of the contributions of precipitation to WY(Kılkış et al., 2021; Y. Li et al., 2022). This research confirmed that the change of precipitation and temperature was the main reason for fluctuation in the independent contributions of CLC to WY and SR change in Liaoning Province.

In recent years, ecosystems around the world have been under extreme stress with the intensifying of global climate change(Hasan & Kumar, 2021; Kılkış et al., 2021; Y. Li et al., 2022). Change of precipitation and temperature were two of the most critical factors in CLC(Alaniz et al., 2022). Changing of precipitation patterns and increasing of temperature can significantly increase the frequency of extreme climate events, which can severely impact regional ESs. Our study confirmed that decreased precipitation and increased of temperature can negatively impact WY and SR. Ecological stresses from water scarcity and drought can severely affect the security of crop production in the region, which lead to a decline in food supply services and ultimately affect the surrounding population's basic subsistence needs(Tripathi et al., 2016). A study have shown that water scarcity had led to a global decline in wheat and maize yield of 5.5% and 3.8% during 1980 and 2010, respectively(Lobell et al., 2011). In addition, for every 0.35°C rising in temperature, there was a 2.75% loss in crop yield, equivalent to 2.4 million tons/year(Hasan & Kumar, 2021). As an agricultural province, Liaoning Province's food security is fundamental to the surrounding area's residents. Active development of relevant climate management policies can significantly improve ESs in Liaoning Province, which can also increase food production and human welfare in the surrounding region. Based on our findings, two suggestions are proposed for Liaoning Province. First, in the western regions within Liaoning province where precipitation is scarce, It’s vital to enhance climatological observation and prediction and to make appropriate management policys for global climate change ahead of schedule. Second, drainage measures must be taken in the heavy precipitation area in the eastern part of Liaoning province. It can prevent severe impacts of extreme precipitation on ESs, which is vital for overall ecosystem stability and the well-being of surrounding residents.

4.2. Impacts of LUCC on ESs

The results of this study showed that LUCC contributed much more to NPP change in Liaoning Province than CLC from 2000 to 2020, and indicated that the dominant factor of NPP change were LUCC. However, our result was different from previous studies, and their studies found that that CLC was the dominant factor for NPP change(Pan et al., 2020; Zhu et al., 2019). In fact, most of their research areas were located in arid regions, where precipitation was independently scarce(Dai et al., 2020), and the vegetation growth in their regions is more susceptible to precipitation. Liaoning Province belongs to monsoon climate of mediium latitudes with abundant precipitation, so the vegetation change may not be very sensitive to precipitation. A s-imilar study also showed that in arid or semi-arid regions, CLC tended to be the main factor in NPP variation(Mahmoud & Gan, 2018). LUCC mainly affected the NPP variation by changing vegetation coverage(Zhao et al., 2020). Forest land is often the ecosystem with the highest vegetation coverage, so it is the main carrier of NPP (Khalifa et al., 2018; Zhao et al., 2020). There are a large amount of mineral resources in the eastern part of Liaoning Province. Driven by interests, people cut down a lot of forests and excavate the bare surface, which significantly reduces the areas of forest land in this region. The results of the scenario simulation framework in this study further showed that the independent contributions of LUCC to NPP change decreased by 6.90% with the loss of forest land area from 2010 to 2020. During 2000 and 2020, rapid urbanization caused the areas of construction land in Liaoning Province to expand about 144.34%, major clustered in the midland of Liaoning Province (Fig. 4), while cropland, forest land and grassland were the main contributors (Fig. 5). When cropland, forest land and grassland was transferred to construction land, WY showed an increasing trend because the urban surface is mainly composed of impervious layer and precipitation directly store as surface runoff (Fig. 10) (Yang et al., 2019). When cropland is transferred to construction land, SR shows an increasing trend due to the reduction of agricultural production activities(Akpoti et al., 2021), while the forest land and grassland transfered to construc-tion land leads a decreasing trend.

Urbanization has become the most critical component of the China's economic development in the past few years(Deyong et al., 2009; Zheng et al., 2022). It significantly promotes economic growth and results in a noticeable increasing in regional GDP and disposable income per capita, which is a crucial way to intensify human walfare(Xiao et al., 2022; Xu et al., 2021). However, human activities have significantly altered the original land use structure and ecological processes. Natural habitats such as cropland, forest land and grassland have been transfered to building land. Natural habitats tend to provide high values of regulating and supporting services, and these are two important services in ensuring regional sustainable development(Yang et al., 2021). Therefore, the loss of natural habitats due to human activities can directly cause a decline in regulating and supporting services, which it gradually deteriorates the ecological environment and ultimately affect the regional and socio-economic progress (Weston et al., 2009; Zhai et al., 2020). The United Nations has also established a new ecosystem management system, the "Transforming our world: the 2030 Agenda for Sustainable Development" plan in 2015 to achieve sustainable development of terrestrial ecosystems(Fang et al., 2021). In addition, a study showed that Guangxi Province in China failed to realize the Sustainable Development Goals (SDGs) 15.2 by 2020 due to urbanization (Qiu et al., 2021). As an essential node in China's "Belt and Road" strategy, Liaoning Province is the most developed province in northeast China. A reasonable land use structure in Liaoning Province not only improves the quality of the ecosystem but also plays a vital part in the sustainable development for circumjacent area. Based on our research results, three suggestions were proposed in Liaoning Province. First, deforestation should be strictly prohibited in the eastern region in Liaoning province. In the meantime, relevant ecosyatem restoration measures, for example natural sealing and reforestation, should be carried out. Second, in suitable areas, we should establish ecological protection zones to restore the ecosystem function of the site to the greatest extent through long-term development as much as possible. Third, the central regions of Liaoning Province where urbanization is the most serious, the local government should strictly control urban expansion and reduce the occupation of natural habitats. Besides, regional ecosystem networks and ecosystem protection models should be established further to optimize the land use structure in the region(Deng et al., 2021).

Assesing and differentiating the independent contributions between CLC and LUCC to ESs change are vital for the increase of ESs and the regional sustainable development. In this study, we constructed the scenario simulation framework by combining the InVEST and the CASA model to assess and differentiate the independent contributions of CLC and LUCC to the ESs change in a dynamic change view. This study showed that NPP, WY and SR in Liaoning Province showed an increasing trend under the combined effect of CLC and LUCC during 2000 and 2020. Among them, the increased areas of NPP value mainly expanded from eastern and wesernt to middle regions, and the spatial distribution of WY was similar to the precipitation. Besides, the increased areas of SR value were mainly clustered in eastern and western regions, showing a similar spatial distribution to the forest land. From 2000 to 2020, CLC was the dominant factor for WY and SR change in Liaoning Province, with an average contributions rate more than 80% (>50%), and LUCC was the dominant factor for NPP change, with an average contributions rate more than 85% (>50%). With the decreased of precipitation and the increased of temperature, the independent contributions of CLC to WY change decreased from 84.02% to 81.79%, and to SR change increased from 73.57% to 85.44%. The change of precipitation condition and the increased of temperature was dominated factor to the fluctuation of the independent contributions of CLC to WY and SR change in Liaoning Province. The independent contributions of LUCC to NPP change decreased from 92.50% to 86.12% with the extensive damage of forest land and accelerated urbanization, revealing the conflict between vigorous economic development and ESs.

CRediT author statement

Junzhu Xiao and Fei Song put forward the idea of this research. Junzhu Xiao wrote the manuscript. Fei Song revised the manuscript. Shuang Song collected the data in this study. Fangli Su provided the funding support.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

This work was supported by the National Key Research and Development Program of China (grant number 2022YFF1301000-4) and the"Xingliao Talents Plan" science and technology innovation leading talents project of Liaoning Province (grant number XLYC2002054).

Acknowledgements

This work accomplished with the help of the Key Laboratory of Soil Erosion and Ecological Restoration of Liaoning Province.

Data Availability

All data generated or analyzed during this study are included in the figures and tables.

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Table 1 Data sources used in this study

Data	Resolution	Time	Data sources
Land use type	30m	2000-2020	GlobeLand30 (http://www.globallandcover.com/)
DEM	30m	2019	Geospatial Data Cloud （http://www.gscloud.cn/）
NDVI (MOD13A1)	500m	2000-2020	MODIS Vegetation Product (https://ladsweb.modaps.eosdis.nasa.gov/)
Landcover (MCD12Q1)	500m	2000-2020	MODIS Vegetation Product (https://ladsweb.modaps.eosdis.nasa.gov/)
Soil data	1km	2016	Harmonized World Soil Database (HWSD) (https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/)
Meteorological data (precipitation、temperature、sunlight)	Excel format	2000-2020	National Meteorological Science Data Center of China (http://data.cma.cn/)

Table 2 Scenario simulation framework

Scenario Hypothesis		Scenario 1	Scenario 2	Scenario 3	Scenario 4
Input Data	Climate	2000	2000	2010	2010
	Land use	2000	2010	2000	2010
Output		Re₁	ES₁	ES₂	ES₃
Scenario Hypothesis		Scenario 5	Scenario 6	Scenario 7	Scenario 8
Input Data	Climate	2010	2010	2020	2020
	Land use	2010	2020	2010	2020
Output		Re₂	ES₄	ES₅	ES₆

Table 3 Land use type area of Liaoning province in 2000-2020

Year	2000		2010		2020
LULC	Area/km²	Ratio (%)	Area/km²	Ratio (%)	Area/km²	Ratio (%)
Cropland	71893.59	49.54	68579.38	47.23	66408.50	45.74
Forest land	41392.66	28.52	42302.77	29.14	43602.67	30.03
Grassland	20192.81	13.91	21244.80	14.63	19662.29	13.54
Water body	2716.17	1.87	3247.98	2.24	2567.29	1.77
Construction land	8626.24	5.94	9352.81	6.44	12176.26	8.39
Unused land	313.15	0.22	460.84	0.32	765.16	0.53

Table 4 ESs change in different scenarios

Scenario Hypothesis		ESs change
Scenario Hypothesis		NPP(gC/m²)	WY(mm/a)	SR(t/km²)
Scenario1(Re₁)		484.76	486.45	26.41
Scenario2(ES₁)	Independent contributions to Re₁	﹢50.31	﹣16.91	﹢0.44
Scenario3(ES₂)		﹢4.08	﹢51.43	﹢1.23
Scenario4(ES₃)		﹢54.39	﹢61.21	﹢1.67
Scenario5(Re₂)		539.15	547.66	28.08
Scenario6(ES₄)	Independent contributions to Re₂	﹢60.48	﹣5.59	﹣0.18
Scenario7(ES₅)		﹢9.75	﹣25.28	﹣1.03
Scenario8(ES₆)		﹢70.23	﹣30.87	﹣1.21

No competing interests reported.

Quantifying the independent contributions of climate and land use change to ecosystem services

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Study area

2.2. Data

2.3. ESs evaluation

2.3.1. NPP

2.3.2. WY

2.3.3. SR

2.4. Scenario simulation framework

3. Result

4. Discussion

4.1. Impacts of CLC on ESs

4.2. Impacts of LUCC on ESs

5. Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1